Selection and Validation of Reference Genes for Gene Expression Studies in an Equine Adipose-Derived Mesenchymal Stem Cell Differentiation Model by Proteome Analysis and Reverse-Transcriptase Quantitative Real-Time PCR
Bibliographic record
Abstract
Adipose-derived stem cells (ADSCs) are used in tissue regeneration therapies. The objective of this study is to identify stable reference genes (RGs) for use in gene expression studies in a characterized equine adipose-derived mesenchymal stem cell (EADMSC) differentiation model. ADSCs were differentiated into adipocytes (ADs) or osteoblasts (OBs), and the proteomes from these cells were analyzed by liquid chromatography tandem mass spectrometry. Proteins that were stably expressed in all three cells types were identified, and the mRNA expression stabilities for their corresponding genes were validated by RT-qPCR. PPP6R1, CCDC97, and then either ACTB or EPHA2 demonstrated the most stable mRNA levels. Normalizing target gene Cq data with at least three of these RGs simultaneously, as per MIQE guidelines (PPP6R1 and CCDC97 with either ACTB or EPHA2), resulted in congruent conclusions. FABP5 expression was increased in ADs (5.99 and 8.00 fold, p = 0.00002 and p = 0.0003) and in OBs (5.18 and 5.91 fold, p = 0.0011 and p = 0.0023) relative to ADSCs. RUNX2 expression was slightly higher in ADs relative to ADSCs (1.97 and 2.65 fold, p = 0.04 and p = 0.01), but not in OBs (0.9 and 1.03 fold, p = 0.58 and p = 0.91).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".